Establishing User Specified Interaction Modes in a Question Answering Dialogue

ABSTRACT

An approach is provided for automatically generating user-specific interaction modes for processing question and answers at the information handling system by receiving a question from a user, extracting user context parameters identifying a usage scenario for the user, identifying first input and output presentation modes for the user based on the extracted user context parameters, monitoring user interaction with the system in relation to the question, and adjusting the first input and output presentation modes based on the extracted user context parameters and detected user interaction with the system.

BACKGROUND OF THE INVENTION

In the field of artificially intelligent computer systems capable ofanswering questions posed in natural language, cognitive questionanswering (QA) systems (such as the IBM Watson™ artificially intelligentcomputer system or and other natural language question answeringsystems) process questions posed in natural language to determineanswers and associated confidence scores based on knowledge acquired bythe QA system. In operation, users submit one or more questions througha front-end application user interface (UI) or application programminginterface (API) to the QA system where the questions are processed togenerate answers or responses that are returned to the user(s). The QAsystem generates multiple hypothesis in the form of answers andassociated confidence measures and supporting evidence by applying anatural language process to an ingested knowledge base (also known asthe corpus) which can come from a variety of sources, including publiclyavailable information and/or proprietary information stored on one ormore servers, Internet forums, message boards, or other online ornetworked information sources. However, the quality of the answer to anindividual user depends in part on what specific information and/oranswer format that user is seeking. With traditional QA systems, thereis only one mode or presentation style for presenting an answer orsystem output, even if presentation styles vary across differentsystems. For example, existing QA systems (e.g., Google search results)retrieve and present information results for browsing, but offer asingle interface mode which is the same for all users. As a result, aconventional QA system will typically provide a single-pass userinterface whereby a user asks a question, and the QA system provides ananswer using the default presentation style. While this is sufficientfor some users, there will be other users who may desire additional (orless) information than is provided with the answer/response using thedefault presentation style. As a result, the existing solutions forprocessing questions and answers provide no opportunity for the user tocontrol or influence the interaction with such QA systems so thatindividualized solutions for efficiently presenting answer responses areextremely difficult at a practical level.

SUMMARY

Broadly speaking, selected embodiments of the present disclosure providea system, method, and apparatus for processing of user inquiries to aninformation handling system capable of answering questions by using thecognitive power of the information handling system to adjust the userinterface interaction based on user context and detected dialog stateinformation between the user and the information handling system. Inselected embodiments, the information handling system may be embodied asa question answering (QA) system which receives and answers one or morequestions from one or more users. To answer a question, the QA systemhas access to structured, semi-structured, and/or unstructured contentcontained or stored in one or more large knowledge databases (a.k.a.,“corpus”). To improve the quality of answers provided by the QA system,a user interface adjustment engine may be configured to extract usercontext information (e.g., user ID, user group, user name, age, gender,date, time, location, input or output device type, mode, name, or IPaddress, topic) associated with a submitted question, and to associatethe extracted user context information with a first user interfaceinteraction mode having specified question assistance and answergranularity options. In addition, the user interface adjustment enginemay be configured to initially process user interactions (e.g., provideanswers or responses) to a user using the first user interfaceinteraction mode. For example, the first user interface interaction modemay specify a question format with question assistance settings whichare activated, including question clarification, question completion,spell correction, and disambiguation, and may also include a specifiedanswer format in which short, individual factoid answers are presented.Based on manually set UI presentation mode preferences and/or detecteddialog state information generated from additional interactions betweenthe user and the information handling system, the user interfaceadjustment engine may be configured to adjust or modify the userinterface interaction mode so that user interactions are processed usinga second, different user interface interaction mode. For example, thesecond user interface interaction mode may specify an answer format inwhich longer, passage-like answers or larger information spaces arepresented, and may also include one or more specified questionassistance settings which are deactivated, including questionclarification, question completion, spell correction, anddisambiguation. Under control of the user interface adjustment enginewhich is continuously applied to detect the user context and dialogstate information, the user interface interaction modes may bedifferentiated across different users to continuously assist the userwith search and discovery by providing an individualized user interfaceto each user with tailored follow-up actions to better accomplish theactual goals for each user.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations, and omissions of detail; consequently;those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present invention, asdefined solely by the claims, will become apparent in the non-limitingdetailed description set forth b-low.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings, wherein:

FIG. 1 depicts a network environment that includes a knowledge managerthat uses a knowledge base and adaptive user interface management formodifying the user interface interaction mode based on detected usercontext and dialog state information;

FIG. 2 is a block diagram of a processor and components of aninformation handling system such as those shown in FIG. 1; and

FIG. 3 illustrates a simplified flow chart showing the logic formodifying the user interface interaction mode based on detected usercontext and dialog state information.

DETAILED DESCRIPTION

The present invention may be a system, a method, and/or a computerprogram product. In addition, selected aspects of the present inventionmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code;etc. or an embodiment combining software and/or hardware aspects thatmay all generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects of the present invention may take theform of computer program product embodied in a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a dynamic or static random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), a magnetic storage device, a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server or cluster of servers. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer (QA) system 100 connected to a computer network 102. TheQA system 100 may include one or more QA system pipelines 100A, 100B,each of which includes a knowledge manager computing device 104(comprising one or more processors and one or more memories, andpotentially any other computing device elements generally known in theart including buses, storage devices, communication interfaces, and thelike) for processing questions received over the network 102 from one ormore users at computing devices (e.g., 110, 120, 130). Over the network102, the computing devices communicate with each other and with otherdevices or components via one or more wired and/or wireless datacommunication links, where each communication link may comprise one ormore of wires, routers, switches, transmitters, receivers, or the like.In this networked arrangement, the QA system 100 and network 102 mayenable question/answer (QA) generation functionality for one or morecontent users. Other embodiments of QA system 100 may be used withcomponents, systems, sub-systems, and/or devices other than those thatare depicted herein.

In the QA system 100, the knowledge manager 104 may be configured toreceive inputs from various sources. For example, knowledge manager 104may receive input from the network 102, one or more knowledge bases orcorpora of electronic documents 106 or other data, a content creator108, content users, and other possible sources of input. In selectedembodiments, the knowledge base 106 may include structured,semi-structured, and/or unstructured content in a plurality of documentsthat are contained in one or more large knowledge databases or corpora.The various computing devices (e.g., 110, 120, 130) on the network 102may include access points for content creators and content users. Someof the computing devices may include devices for a database storing thecorpus of data as the body of information used by the knowledge manager104 to generate answers to cases. The network 102 may include localnetwork connections and remote connections in various embodiments, suchthat knowledge manager 104 may operate in environments of any size,including local and global, e.g., the Internet. Additionally, knowledgemanager 104 serves as a front-end system that can make available avariety of knowledge extracted from or represented in documents,network-accessible sources and/or structured data sources. In thismanner, some processes populate the knowledge manager, with theknowledge manager also including input interfaces to receive knowledgerequests and respond accordingly.

In one embodiment, the content creator creates content in an electronicdocument for use as part of a corpora of data 107 with knowledge manager104. Content may also be created and hosted as a document in one or moreremote databases 165, 175 separately from the QA system 100A. Whereverstored, the corpora may include any structured and unstructureddocuments, including but not limited to any file, text, article, orsource of data (e.g., scholarly articles, dictionary definitions,encyclopedia references, and the like) for use in knowledge manager 104.Content users may access knowledge manager 104 via a network connectionor an Internet connection to the network 102, and may input questions toknowledge manager 104 that may be answered by the content in the corpusof data. As further described below, when a process evaluates a givensection of a document for semantic content 108, the process can use avariety of conventions to query it from the knowledge manager. Oneconvention is to send a well-formed question 10. Semantic content iscontent based on the relation between signifiers, such as words,phrases, signs, and symbols, and what they stand for, their denotation,or connotation. In other words, semantic content is content thatinterprets an expression, such as by using Natural Language (NL)Processing. In one embodiment, the process sends well-formed questions10 (e.g., natural language questions, etc.) to the knowledge manager104. Knowledge manager 104 may interpret the question and provide aresponse to the content user containing one or more answers 20 to thequestion 10. In some embodiments, knowledge manager 104 may provide aresponse to users in a ranked list of answers 20.

In some illustrative embodiments, QA system 100 may be the IBM Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The IBM Watson™ knowledge managersystem may receive an input question 10 which it then parses to extractthe major features of the question, that in turn are used to formulatequeries that are applied to the corpus of data stored in the knowledgebase 106. Based on the application of the queries to the corpus of data,a set of hypotheses, or candidate answers to the input question, aregenerated by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question.

In particular, a received question 10 may be processed by the IBMWatson™ QA system 100 which performs deep analysis on the language ofthe input question 10 and the language used in each of the portions ofthe corpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e., candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. The QA system 100 thengenerates an output response or answer 20 with the final answer andassociated confidence and supporting evidence. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

In addition to providing answers to questions, QA system 100 may embodyan adaptive user interface management engine or module 13 within theknowledge manager 104 which processes user inquiries to adjust the userinterface interaction based on detected user context and dialog stateinformation. To improve the quality of answers provided by the QA system100, the adaptive user interface management engine/module 13 may beembodied as part of a QA information handling system 14 in the knowledgemanager 104, or as a separate information handling system, to extractuser context information (e.g., user ID, user group, user name, age,gender, date, time, location, input or output device type, mode, name,or IP address, topic) associated with a submitted user/question, and toassociate the extracted user context information with a first or defaultuser interface question mode 11 having specified question assistanceoptions. In addition or in the alternative, the embodied adaptive userinterface management engine/module 13 may associate the extracted usercontext information with a first or default user interface answer mode12 having specified answer granularity options. An example default userinterface question mode 11 may specify a plurality of system inputinteraction controls to facilitate user input, including, but notlimited to, “Did you mean” assistance, question completion assistance,automatic spell correction, “always on” listening mode, disambiguationassistance, and the like. In addition, the default user interfacequestion mode 11 may include one or more actionable user feedback toolsto collect user feedback about desired presentation modes, including butnot limited to natural language corrections to what the system showed,click-through frequencies for various presentation styles, and other UIcontrols that can be designed for actionable presentation. Based on userresponses collected over time through actionable feedback about thedesired user interface presentation mode, the adaptive user interfacemanagement engine/module 13 may be configured to adjust or modify thefirst or default user interface answer mode 12, starting with a UIpresentation mode that is designed to satisfy the average user, and thenbecoming more personalized over time as the adaptive user interfacemanagement engine/module 13 has more interaction with a specific user.In this way, the adaptive user interface management engine/module 13processes a user's first interaction to detect a user context (e.g., ona computer, on a mobile device, while moving, etc.) and assign a defaultinput and output mode 11, 12 for the initial question/answerinteraction. However, the adaptive user interface managementengine/module 13 also processes user interactions to manually orautomatically tune or adjust the input/output modes 11, 12 as the userinteracts with the system. As indicated with the cascaded UI questionmodes 11 and cascaded UI answer modes 12, the modified input/outputmodes 11, 12 may by selected from a plurality of existing, predefined UIinput and/or output modes 11, 12, or may be generated by modifying oradjusting one or more specified question assistance settings orspecified answer granularity options in the default input/output modes11, 12.

To initiate processing, each received question 10 may first be routedthrough an intelligent question routing process which directs thequestion 10 to the correct processing function or module within theknowledge manager 104. For example, the QA information handling system14 in the knowledge manager 104 may process the question 10 with anextraction process, such as a semantic analysis tool or automaticauthorship profiling tool, to extract user context information (e.g.,user ID, user group, user name, age, gender, date, time, location, inputor output device type, mode, name, or IP address, topic) associated withthe user that submitted the question 10. The extracted user contextinformation may be used to categorize the usage scenario in terms ofinput device (e.g., mobile v. tablet v. desktop/laptop), input modality(e.g., voice v. keyboard v. touchpad), position and movement (e.g., homev. on the road v. at the office), and/or user attributes (e.g., age orexperience with the system). The QA information handling system 14 mayalso use natural language (NL) processing to analyze textual informationin the question and retrieved information from the knowledge database106 in order to extract or deduce question context information relatedthereto, and to determine if the submitted question or topic hassufficient supporting content and training in the knowledge database106. If the received question 10 is understood and is directed to atopic having sufficient supporting content and training, the QAinformation handling system 14 may use NLP processing to analyze textualinformation in the question and retrieve responsive information from theknowledge database 106, where “NLP” refers to the field of computerscience, artificial intelligence, and linguistics concerned with theinteractions between computers and human (natural) languages. In thiscontext, NLP is related to the area of human-to-computer interaction andnatural language understanding by computer systems that enable computersystems to derive meaning from human or natural language input.

To process the received question 10, the adaptive user interfacemanagement processing function or module 13 which may use NL processingto select an initial or default user interface question mode. To thisend, the adaptive user interface management processing function/module13 may be configured to select a first user interface question mode 11which includes specified question assistance settings which areactivated, including but not limited to question clarification, questioncompletion, spell correction, and disambiguation. In addition, a firstuser interface answer mode 12 may be selected by the adaptive userinterface management processing function/module 13 which includesspecified answer settings which are activated, including but not limitedto, a cache lookup mode, a “short” or “long” format answer mode, achannel bandwidth setting, an “audio mode” setting, a “display mode”setting, a search hit web link listing setting, and/or user feedbacktool settings. To improve the quality of answers provided by the QAsystem 100, the adaptive user interface management processingfunction/module 13 may be configured to continually or periodicallymonitor user feedback to detect dialog state information between theuser and the QA information handling system 14. Over time, the adaptiveuser interface management processing function/module 13 is trainedthrough user feedback to choose UI question and answer modes forparticular question types and/or users, starting with a presentationintended to satisfy the average user and becoming more personalized overtime as the QA information handling system 14 has more interaction witha specific user.

Types of information handling systems that can use the QA system 100range from small handheld devices, such as handheld computer/mobiletelephone 110 to large mainframe systems, such as mainframe computer170. Examples of handheld computer 110 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation handling systems include pen, or tablet, computer 120,laptop, or notebook, computer 130, personal computer system 150, andserver 160. As shown, the various information handling systems can benetworked together using computer network 102. Types of computer network102 that can be used to interconnect the various information handlingsystems include Local Area Networks (LANs), Wireless Local Area Networks(WLANs), the Internet, the Public Switched Telephone Network (PSTN),other wireless networks, and any other network topology that can be usedto interconnect the information handling systems. Many of theinformation handling systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the information handlingsystems may use separate nonvolatile data stores (e.g., server 160utilizes nonvolatile data store 165, and mainframe computer 170 utilizesnonvolatile data store 175). The nonvolatile data store can be acomponent that is external to the various information handling systemsor can be internal to one of the information handling systems. Anillustrative example of an information handling system showing anexemplary processor and various components commonly accessed by theprocessor is shown in FIG. 2.

FIG. 2 illustrates information handling system 200, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein. Information handling system 200 includes one or more processors210 coupled to processor interface bus 212. Processor interface bus 212connects processors 210 to Northbridge 215, which is also known as theMemory Controller Hub (MCH). Northbridge 215 connects to system memory220 and provides a means for processor(s) 210 to access the systemmemory. In the system memory 220, a variety of programs may be stored inone or more memory device, including an adaptive user interface enginemodule 221 which may be invoked to process user interactions to adjustthe user interface interaction based on user context and detected dialogstate information between the user and the information handling system.Graphics controller 225 also connects to Northbridge 215. In oneembodiment, PCI Express bus 218 connects Northbridge 215 to graphicscontroller 225. Graphics controller 225 connects to display device 230,such as a computer monitor.

Northbridge 215 and Southbridge 235 connect to each other using bus 219.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 215and Southbridge 235. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 235, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 235typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 296 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (298) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. Othercomponents often included in Southbridge 235 include a Direct MemoryAccess (DMA) controller, a Programmable Interrupt Controller (PIC), anda storage device controller, which connects Southbridge 235 tononvolatile storage device 285, such as a hard disk drive, using bus284.

ExpressCard 255 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 255 supports both PCI Expressand USB connectivity as it connects to Southbridge 235 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 235 includesUSB Controller 240 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 250, infrared(IR) receiver 248, keyboard and trackpad 244. and Bluetooth device 246,which provides for wireless personal area networks (PANs). USBController 240 also provides USB connectivity to other miscellaneous USBconnected devices 242, such as a mouse, removable nonvolatile storagedevice 245, modems, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 245 is shown as a USB-connected device,removable nonvolatile storage device 245 could be connected using adifferent interface, such as a Firewire interface, etc.

Wireless Local Area Network (LAN) device 275 connects to Southbridge 235via the PCI or PCI Express bus 272. LAN device 275 typically implementsone of the IEEE 802.11 standards for over-the-air modulation techniquesto wireless communicate between information handling system 200 andanother computer system or device. Extensible Firmware Interface (EFI)manager 280 connects to Southbridge 235 via Serial Peripheral Interface(SPI) bus 278 and is used to interface between an operating system andplatform firmware. Optical storage device 290 connects to Southbridge235 using Serial ATA (SATA) bus 288. Serial ATA adapters and devicescommunicate over a high-speed serial link. The Serial ATA bus alsoconnects Southbridge 235 to other forms of storage devices, such as harddisk drives. Audio circuitry 260, such as a sound card, connects toSouthbridge 235 via bus 258. Audio circuitry 260 also providesfunctionality such as audio line-in and optical digital audio in port262, optical digital output and headphone jack 264, internal speakers266, and internal microphone 268. Ethernet controller 270 connects toSouthbridge 235 using a bus, such as the PCI or PCI Express bus.Ethernet controller 270 connects information handling system 200 to acomputer network, such as a Local Area Network (LAN), the Internet, andother public and private computer networks.

While FIG. 2 shows one information handling system, an informationhandling system may take many forms, some of which are shown in FIG. 1.For example, an information handling system may take the form of adesktop, server, portable, laptop, notebook, or other form factorcomputer or data processing system. In addition, an information handlingsystem may take other form factors such as a personal digital assistant(PDA). a gaming device, ATM machine, a portable telephone device, acommunication device or other devices that include a processor andmemory. In addition, an information handling system need not necessarilyembody the north bridge/south bridge controller architecture, as it willbe appreciated that other architectures may also be employed.

To provide additional details for an improved understanding of selectedembodiments of the present disclosure, reference is now made to FIG. 3which depicts a simplified flow chart 300 showing the logic formodifying the user interface interaction mode based on detected usercontext and dialog state information. The processing shown in FIG. 3 maybe performed in whole or in part by a cognitive system, such as theadaptive user interface management engine or module 13 embodied in acognitive system 100. such as an IBM Watson™ QA system or other naturallanguage question answering system, which modifies the user interfaceinteraction mode for an individual user to provide an individualizeduser interface with tailored follow-up actions to better accomplish theactual user goals based on detected the user context and dialog stateinformation.

FIG. 3 processing commences at 301 whereupon, at step 302, a useraccesses a system, such as by registering, logging in, or otherwisesending an access or search request to a system. Step 302 may beperformed at the QA system 100 or other NLP question answering system asan initial processing step wherein the user access is captured,processed, and stored. The specific access procedures and mechanisms mayvary from one system to the next, though the system access may enabledetection of the usage scenario associated with the user.

To provide the user with dynamically adapted user interface experience,the QA system 100 or other NLP question answering system may employ anadaptive user interface process 303 which may be continuously orperiodically applied to mine user interactions and modify thepresentation modes by using different categories of informationpresentation paired with user feedback tools providing actionableaffordances to collect user feedback about desired presentation modes.The range of user feedback tools include natural language corrections towhat the system showed, click-through frequencies for variouspresentation styles, and other UI controls that can be designed foractionable presentation. Over time, the presentation mode chosen forparticular question types and/or users is trained by starting with afirst or default presentation mode intended to satisfy the average user,and then adapting the first/default presentation mode to become morepersonalized over time as the system has more interaction with aspecific user. For example, when a user first interacts with the systemin a given context (on a computer, on a mobile device, while moving,etc.), the user is assigned a default input and output mode with definedpreference settings that the system will use for interaction with theuser. By monitoring the user's subsequent system interactions and/ormanual tuning of the defined preference settings, the system may learnhow to modify the input/output modes from the user's interactions sothat the user-defined and pre-defined modes can be modified to behave inuser-desired ways. By having different interaction settings both forquestion input and answer output, the user can have a more enrichedexperience specifically tailored to their knowledge of the system andexpected results. In selected embodiments, the question inputpreferences can define features such as content-assist and cachelookups, and the answer output preferences can define dialogueproperties, answer type and presentation, and ability to search anddiscover additional information.

As a preliminary step in the adaptive user interface process 303, a usercontext extraction step 304 is performed to extract user contextinformation relating to the usage scenario for the user. The usercontext extraction processing at step 304 may be performed at the QAsystem 100 or other NLP question answering system as an initialprocessing step wherein an extraction process uses a multimodal userinterface (UI) or application programming interface (API) to processmultimodal input questions 10 to effectively transform the differentinputs to a shared or common format for user context extractionprocessing. At this input stage, the extraction processing at step 304may be suitably configured to understand or determine the user profile,user ID, user group, user name, age, gender, location (which can bedetected using the GPS on their mobile devices or approximation using IPaddress), date and time information for each of the end users, type ofinput or output device used to submit a question, and/or device mode(e.g., audio or visual settings), thereby generating user contextinformation for each user. For example, the processing at step 304 mayapply a semantic analysis tool or automatic authorship profiling tool toobtain user profile information for the end user accessing the system.In selected example embodiments, the extraction processing at step 304may generate user context information by leveraging location informationof each end user, such as by detecting specific end user locationinformation (e.g., GPS coordinates) based on the end user devicecapabilities, and/or by detecting approximation-based end user locationinformation (e.g., origination IP address). In other embodiments, thecontext extraction processing step 304 may identify additionalcontextual information for each submitted question, such as key terms,focus, lexical answer type (LAT) information, sentiment, synonyms,and/or other specified terms. The user context extraction process 304may also evaluate the extracted user context information to identify acorresponding usage scenario. For example, the user context extractionprocess 304 may employ an NLP routine, machine learning tool, artificialintelligence, or other linguistics process to specify a usage scenarioin terms of an input device parameter (e.g., mobile v. tablet v.desktop/laptop), input modality parameter (e.g., voice v. keyboard v.touchpad), position and movement parameter (e.g., home v. on the road v.at the office), and/or user attribute parameter (e.g., age or experiencewith the system). As will be appreciated, other usage parameters may beused to characterize the usage scenario at step 304.

Once the usage scenario parameters are identified, the user interfacemode selection process 305 may select a first or default UI interactionmode by mapping a combination of detected usage scenario parameters toan initial or default set of input and output modes, such as a questionpresentation mode and/or answer presentation mode, that the system willuse. Other selection mechanisms may be employed, including but notlimited to classifying the extracted usage scenario parameters into adefault UI mode, using Bayesian reasoning to make inferences fromextracted usage scenario parameters when selecting a default UI mode,and the like. The processing at step 305 may be performed at the QAsystem 100 or other NLP question answering system and applied to theextracted usage scenario parameters, though any desired informationprocessing system for selecting a UI interaction mode may be used. Asdescribed herein, a Natural Language Processing (NLP) routine may beused to process the extracted user context and/or usage scenarioparameters to select a UI interaction mode. In this context, NLP isrelated to the area of human-computer interaction and natural languageunderstanding by computer systems that enable computer systems to derivemeaning from human or natural language input. In selected embodiments. adefault UI input mode and default UI output mode are selected at step305 for submitting the initial question and answer responses at thesystem, where the default UI input and output modes are configured withspecified question and answer assistance settings which are set in a“learning mode” in which the user interactions with the system willinfluence how the system input and output modes behave in the future.For example, the selection processing at step 305 may select a defaultUI input mode to facilitate question submissions by activating specifiedquestion assistance settings which facilitate or ease the userexperience, such as question clarification assistance (a.k.a., “did youmean” assistance), question completion assistance, automatic spellcorrection, listening mode assistance, and/or disambiguation assistance.In addition, the selection processing at step 305 may select a defaultUI output mode to facilitate answer responses by activating specifiedanswer assistance settings for formatting the system response, such as acache lookup mode setting, a “short” or “long” format answer modesetting, a channel bandwidth setting, an “audio mode” setting and/or“display mode” setting for specifying the audio/textual/image/videocontent for the answer, a search hit web link listing setting to displaysearch hits to web links in a way that enables information exploration,a related application setting for launching an application within theanswer, and/or user feedback settings to present a variety of “relatedquestions” or “did you mean” options and content assistance features. Aswill be appreciated, the default UI output mode may be configured toprovide a variety of different output presentation modes which can beformatted to return answer responses in a variety of ways.

Using the selected default UI input mode, the user submits a question tothe system which is received and processed at step 306 to generate ananswer with associated evidence and confidence measures for the enduser(s). The reception processing at step 306 may be performed at the QAsystem 100 or other NLP question answering system, though any desiredinformation processing system for receiving and processing questions andanswers may be used. As described herein, a Natural Language Processing(NLP) routine may be used to process the received questions and/orgenerate a computed answer with associated evidence and confidencemeasures. In this context, NLP is related to the area of human-computerinteraction and natural language understanding by computer systems thatenable computer systems to derive meaning from human or natural languageinput. For example, a received question might ask “Who was John QuincyAdams' Vice President?” or “What should I get my husband for a birthdaypresent?”

At step 307, the answer response to the submitted question is presentedto the user using the selected default UI output mode. The presentationprocessing at step 307 may be performed at the QA system 100 or otherNLP question answering system, though any desired information processingsystem for receiving and processing questions and answers may be used.As will be appreciated, a range of different answers may be provided inresponse to a question. For example, correct system responses to thequestion, “Who was John Quincy Adams' Vice President?” could include ashort, factoid answer with the person's name (“John C. Calhoun”) orcould a broader biographical description of the John Calhoun. Theaccuracy or correctness of the response will depend on the goal behindthe user's question. For example, someone trying to settle a bar betusing a mobile device with a small screen may want only the person'sname, while a student researching a term paper will likely wantadditional information. As another example, there are a variety ofcorrect responses to the question, “What should I get my husband for abirthday present?” some of which may include photos of suggested objectsand others which include feature comparison grids.

As shown by the double-sided arrow between step 306 and step 307, morethan one question and answer may be submitted using the selected UIinput and output modes as part of the user interaction monitoringprocess. In the initial pass, default UI input and output modes will beused, but as described below, subsequent passes may use modified UIinput/output modes to submit questions and answers within the adaptiveuser interface process 303.

As seen from these example questions and answers, the value or qualityof an answer will depend on the user's underlying intention or goalbehind the question. The user's end goal is very important indetermining whether one or more of these presentation modes arepreferred. Unfortunately, the user's end goal cannot readily beascertained from a straightforward question analysis or by applicationof conventional artificial intelligence tools. Nor can a questionanalysis assess whether some or all of the question assistance or answersettings may be helpful, distracting and/or time-consuming to the user.To better assess the user's intention or goals and the usefulness of thequestion assistance or answer settings, the quantity and modality of theuser's interactions with the system may be monitored to help the systemlearn which question or answer presentation mode is most helpful ordesired for a given context. When the user interacts with informationpresented in a first presentation mode, that interaction is recorded asa success for the particular mode given the usage scenario. In othercases, the user may explicitly correct the system's choice ofpresentation mode via natural language or UI controls, which case thatinteraction is recorded as a correction or modification input for theparticular mode given the usage scenario.

To track user responses, the selected default UI input and/or outputmodes may be defined to include one or more actionable user feedbacktools which are monitored to detect dialog state information between theuser and the information handling system. In particular, once the userresponds to the answer response by activating one of the user feedbacktools, the received user feedback/response may be processed at step 308to evaluate the user's response to the original answer/response fromstep 307. The processing at step 308 may be performed at the QA system100 or other NLP question answering system and applied to the user'sresponse or feedback, though any desired information processing systemfor processing user feedback or responses may be used. In selectedembodiments, a Natural Language Processing (NLP) routine may be used toprocess the user feedback and/or user responses to evaluate the user'ssatisfaction with the original answer/response based on user responsesto what the system showed, user click-through behavior, follow-upquestions from the user, and/or other UI presentation mode indicators.Through the processing of the user's feedback/response at step 308,question and answering dialogue state information is captured that maybe used to define the user interaction between the user and theinformation handling system.

Based on the captured dialog state information, the question and/oranswer presentation modes may be modified or changed to better reflectthe user's underlying intentions or goals behind the submittedquestions. In the adaptive user interface process 303. the presentationmodes are adapted or changed at step 309 by changing the user interfaceinput/output mode based on the detected dialog state extracted from theuser feedback/response and the detected user context/usage scenario. Asdescribed herein, the presentation modes can be adapted or modifiedmanually (e.g., when the user manually changes the preference settings)and/or automatically (e.g., by applying an automated learning algorithmor process which processes user interactions and/or usage scenarioinformation). In either case, the processing at step 309 may beperformed at the QA system 100 or other NLP question answering systemand applied to adapt or modify the UI input/output mode based the userfeedback/response and user context/usage scenario, though any desiredinformation processing system for modifying the user interfaceinput/output mode may be used. In selected embodiments, the processingstep 309 uses a learning routine or algorithm to modify or adapt the UIinput/output mode based on the identified usage scenario (such asextracted from the user profile) and/or the identified dialog stateinformation (such as captured from the user feedback responses or otherinteractions with the system). In selected embodiments, the processingat step 309 may apply a collaborative filtering tool or algorithm thatfilters for information or patterns by collaborating among multipleagents, viewpoints, data sources, etc., to make automatic associations(filtering) between questions and/or answer for different end users(collaborating). thereby predicting a preferred input or output modebased on one or more features of the usage scenario. For example, thecollaborative filtering may be applied to examine user profile featuresextracted for a particular user when assessing whether the user has anydistinct or unique property that would indicate selecting a differentinformation mode for the presentation. In addition or in thealternative, the collaborative filtering applied at step 309 may beapplied to examine the dialog state between the user and system—such asuser feedback (e.g., “like” or “dislike” answer ratings for thepresentation mode that the user received) or other user interaction—whenassessing whether the system dialog state indicates selecting adifferent presentation mode for the user.

In adapting the input/output presentation mode, the process step 309 mayuse the dialog state information from user interactions, along withusage scenario information from user context or profile information, toassess the informational sufficiency of different presentation styles interms of satisfying the user's goal or intention behind the question.For example, user interactions with the default UI output mode can beanalyzed to determine whether the user simply wants to know a fact orpurchase something or read a web page about a historical event. In eachof these cases, the optimal presentation of information will bedifferent, and can be detected by monitoring the dialog state and/orusage scenario so that the UI input/output mode will be modifiedaccordingly. In addition, user context or profile information can beanalyzed to determine a usage scenario, such as the type ofinterface/communication channel being used. In the case of acommunication channel to a mobile device, the optimal presentation ofinformation will use a smaller, more condensed information presentationas compared to a broadband channel to a desktop computer with a largescreen for text/visual presentation, and the UI input/output mode willbe modified accordingly. In similar fashion, if the detected usagescenario is for a device with only an audio channel open, the UIinput/output mode may be modified to account for this usage.

In selected embodiments, the adaptation of the input/output presentationmode at process step 309 may be implemented by selecting betweenuser-defined and/or predefined UI presentation modes to allow the QAsystem to behave in user-desired ways. By configuring predefined UIpresentation modes with different interaction settings both for questioninput and retrieval and then selecting a desired UI presentation mode atthe adaptation step 309, the user can have a more enriched experiencespecifically tailored to their knowledge of the system and expectedresults. For example, the predefined UI input (or question) presentationmodes may be variously configured with input preferences to definequestion assistance settings, such as question clarification, questioncompletion, spell correction, and disambiguation. Likewise, thepredefined UI output (or answer) presentation modes may be variouslyconfigured with output preferences to define answer features and/orrelated dialog properties, such as a cache lookup mode, answer type andpresentation, the ability to search and discover additional informationor open related applications.

After modifying the UI input/output mode at step 309, the process endsat step 311, at which point the adaptive user interface process 303 mayawait reactivation according to a predetermined or periodic activationschedule. Alternatively and as indicated by the feedback line 310 to theprocessing step 306, the adaptive user interface process 303 may beiteratively applied to a sequence of questions and answers over time totrain the UI presentation modes chosen for particular question types oruses, starting with a default UI input/output presentation mode thatintended to satisfy the average user, and then becoming morepersonalized over time as the system has more interaction and/orfeedback with a specific user. In contrast, a conventional QA system,such as the Google search engine, offers a static and unchanging UIoutput mode listing of multiple search result options for possiblefollow-up action which is the same for all users and which has limitedor non-existent user feedback options for indicating which mode was theright one. By iteratively adapting the input/output presentation modeover time, the process step 309 enables differentiation of the UIpresentation modes across different user modes to accomplish differentand evolving user goals, while providing richer information forfollow-up actions to assist the user with search and discovery.

To illustrate how the adaptive user interface process 303 can learn fromthe user's interactions and user context to modify the UI presentationmodes, a first example use case will now be described with reference toa first user using a desktop home computer to submit a question over theInternet. In this first example use case, the extracted user contextinformation resulting from step 304 specifies a first usage scenario interms of a first set of extracted user context parameters (e.g.,Context: desktop browser, keyboard, at home). Based on the extracteduser context information, a first default UI input mode selected at step305 is specified in terms of a first set of default input modeparameters (e.g., Default input mode: automatic spell correction, “didyou mean” assistance, question completion, disambiguation assistance,listen-mode off). In addition, the selection processing step 305specifies a first default UI output mode based on the extracted usercontext information in terms of a first set of default output modeparameters (e.g., Default output mode: presenting many search hits toweb links in a way that enables information exploration, textual andimage results). Using the selected default input/output modes, one ormore questions and answers are captured and processed at steps 306, 307,thereby collecting user feedback through the captured question andanswer interaction. For example, the interaction may include thefollowing sequence of questions: User: “Is United flight 522 ontime?”//System: “United Airlines?”//User: “Yes. Of course!”//System:“United Airlines flight 522 will be landing at 7:30 PM.”//User: “Yes orno?”//System: “Yes. United Airlines flight 522 is on time. In responseto the first question (“Is United flight 522 on time?”), the system mayuse the disambiguation assistance parameter setting from the firstdefault UI input mode to ask for clarification, “United Airlines?” Inresponse, the user provides confirmation feedback, “Yes. Of course!” Inresponse, the system may use a defined parameter setting from the firstdefault UI output mode to provide a first, expansive response, “UnitedAirlines flight 522 will be landing at 7:30 PM.” In response, the userprovides negative feedback “Yes or no?” In response, the system may usea defined parameter setting from the first default UI output mode toprovide a second, more focused response, “Yes. United Airlines flight522 is on time.” At step 308, the captured question and answerinteraction is processed to assess a dialog state which indicates thatthe user does not require disambiguation assistance when submittingquestions, and also indicates that the user prefers “Yes” or “No”answers when possible. At step 309, this dialog state information isused to adapt the UI input mode to deactivate the disambiguationassistance feature, and to adapt the UI output mode to answer withYes/No when possible.

To illustrate another example of the adaptive user interface process 303learning how to modify the UI presentation modes from the user'sinteractions and user context, a second example use case will now bedescribed with reference to a second user using a mobile device tosubmit a question using voice commands while driving. In this secondexample use case, the extracted user context information resulting fromstep 304 specifies a usage scenario in terms of a second set ofextracted user context parameters (e.g., Context: mobile device, usingvoice commands, while driving). Based on the extracted user contextinformation, a second default UI input mode selected at step 305 isspecified in terms of a second set of default input mode parameters(e.g., Default input mode: automatic spell correction, always on listenmode, no disambiguation). In addition, the selection processing step 305specifies a second default UI output mode based on the extracted usercontext information in terms of a second set of default output modeparameters (e.g., Default output mode: verbal, short answer). Using theselected default input/output modes, one or more questions and answersare captured and processed at steps 306, 307, thereby collecting userfeedback through the captured question and answer interaction. Forexample, the interaction may include the following sequence ofquestions: User: “Where is the nearest gas station?”//System: “There isa gas station at 100 Highway 1, Somerville, N.Y.”//User: “Can you showit on the map?”//System: <displays map with directions>. In response tothe first question (“Where is the nearest gas station?”), the system mayuse the parameter settings from the second default UI output mode toprovide a short, concise verbal description of the location in theanswer (“There is a gas station at 100 Highway 1, Somerville, N.Y.”).recognizing from the detected context that the user is driving andcommunicating over a limited channel width ask mobile device. However,in response to the user's feedback response (“Can you show it on themap?”), the system may use a defined parameter setting from the seconddefault UI output mode to display a map with directions to the requestedlocation. At step 308, the captured question and answer interaction isprocessed to assess a dialog state which indicates that the user wishesto open a map application when requesting location information whiledriving and using a mobile device. At step 309, this dialog stateinformation is used to adapt the UI output mode to use a map applicationwhen answering a question that requests location information whiledriving and using a mobile device.

To illustrate yet another example of the adaptive user interface process303 learning how to modify the UI presentation modes from the user'sinteractions and user context, a third example use case will now bedescribed with reference to a third user who submits a question over theInternet from a desktop home computer. In this third example use case,the extracted user context information resulting from step 304 specifiesa usage scenario in terms of a third set of extracted user contextparameters (e.g., Context: desktop browser, keyboard, at home). Based onthe extracted user context information, a third default UI input modeselected at step 305 is specified in terms of a third set of defaultinput mode parameters (e.g., Default input mode: automatic spellcorrection, “did you mean” assistance, question completion,disambiguation assistance, listen-mode off). In addition, the selectionprocessing step 305 specifies a third default UI output mode based onthe extracted user context information in terms of a second set ofdefault output mode parameters (e.g., Default output mode: presentingmany search hits to web links in a way that enables informationexploration, textual and image results). Using the selected defaultinput/output modes, one or more questions and answers are captured andprocessed at steps 306, 307, thereby collecting user feedback throughthe captured question and answer interaction. For example, theinteraction may include the following sequence of questions: User: “Whatshould I buy for my husband on his birthday?”//System: “Here are somelinks for men's birthday presents: <links>”//User: “Can you show mepictures of the presents?”//System: “Here are some images of men'sbirthday presents: <images>”. In response to the first question (“Whatshould I buy for my husband on his birthday?”), the system may use theparameter settings from the third default UI output mode to providesearch results with a listing of web links to enable informationexploration (“Here are some links for men's birthday presents:<links>”), recognizing from the detected context that the user allowsfor computationally intensive user interactions at the home desktopcomputer environment. However, in response to the user's feedbackresponse (“Can you show me pictures of the presents?”), the system mayuse a defined parameter setting from the third default UI output mode todisplay images of the requested information. At step 308, the capturedquestion and answer interaction is processed to assess a dialog statewhich indicates that the user wishes to be presented with images for anytopic relating to purchasing requests. At step 309, this dialog stateinformation is used to adapt the UI output mode to display images aspart of the answer or response to a question topic relating to shoppingor purchasing.

By now, it will be appreciated that there is disclosed herein a system,method, apparatus, and computer program product for generatinguser-specific interaction modes for processing question and answers withan information handling system having a processor and a memory. Asdisclosed, the system, method, apparatus, and computer program productreceive a question from a user and extract or identify therefrom aplurality of user context parameters identifying a usage scenario forthe user. Examples of user context parameters include, but are notlimited to the user's input device type, output device type, inputmodality, location, and one or more user attributes selected from agroup consisting of age, experience, demography, and topic. Based on theextracted user context parameters. the system identifies or selects afirst input presentation mode and a first output presentation mode forthe user. In selected embodiments, the first input and outputpresentation modes may be identified by selecting a default inputpresentation mode having specified question assistance settings andselecting a default output presentation mode having specified answergranularity options. For example, the specified question assistancesettings may be selected from a group consisting of a questionclarification setting, question completion setting, spell correctionsetting, and disambiguation assistance setting. In addition, thespecified answer granularity options may be selected from a groupconsisting of an enable exploration setting, a web link setting, acontext setting, a precise response setting, a verbose response setting,an alternative response setting, a supporting evidence setting, an imagesetting, and an application launch setting. As information and responsesare exchanged in relation to the question using the first input andoutput presentation modes, the user interactions are monitored and usedto adjust the first input and output presentation modes. For example,the user feedback and responses provided using the first input andoutput presentation modes may be monitored to assess a dialog stateindicating a user's desired presentation mode, such as by detectingnatural language corrections by the user to responses provided by thesystem using the first input and output presentation modes, monitoringuser click-through frequencies for responses provided by the systemusing the first input and output presentation modes, and/or detectinguser activation of a user interface control included in the first inputand output presentation modes. In selected embodiments, the first inputand output presentation modes may be adjusted by modifying one or moreof the specified question assistance settings or specified answergranularity options.

While particular embodiments of the present invention have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, changes and modifications may be madewithout departing from this invention and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this invention. Furthermore, it is to be understood that theinvention is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to inventions containing only one such element,even when the same claim includes the introductory phrases “one or more”or “at least one” and indefinite articles such as “a” or “an”; the sameholds true for the use in the claims of definite articles.

1. A method, in an information handling system comprising a processorand a memory, for generating user-specific interaction modes forprocessing question and answers at the information handling system, themethod comprising: receiving, by the system, a question from a user;extracting, by the system, a plurality of user context parametersidentifying a usage scenario for the user; identifying, by the system, afirst input presentation mode and a first output presentation mode forthe user based on the plurality of user context parameters extracted forthe user; monitoring, by the system, user interaction with the system inrelation to the question; and adjusting, by the system, the first inputand output presentation modes based on the user interaction with thesystem.
 2. The method of claim 1, wherein extracting the plurality ofuser context parameters comprises identifying, by the system, the user'sinput device type, output device type, input modality, location, and oneor more user attributes selected from a group consisting of age,experience, demography, and topic.
 3. The method of claim 1, whereinidentifying the first input and output presentation modes comprisesselecting a default input presentation mode comprising specifiedquestion assistance settings and selecting a default output presentationmode comprising specified answer granularity options.
 4. The method ofclaim 3, wherein the specified question assistance settings are selectedfrom a group consisting of a question clarification setting, questioncompletion setting, spell correction setting, and disambiguationassistance setting.
 5. The method of claim 3, wherein the specifiedanswer granularity options are selected from a group consisting of anenable exploration setting, a web link setting, a context setting, aprecise response setting, a verbose response setting, an alternativeresponse setting, a supporting evidence setting, an image setting, andan application launch setting.
 6. The method of claim 1, whereinmonitoring user interaction with the system comprises monitoring userfeedback and responses provided using the first input and outputpresentation modes to assess a dialog state indicating a user's desiredpresentation mode.
 7. The method of claim 6, wherein monitoring userfeedback and responses comprises detecting natural language correctionsby the user to responses provided by the system using the first inputand output presentation modes, monitoring user click-through frequenciesfor responses provided by the system using the first input and outputpresentation modes, and detecting user activation of a user interfacecontrol included in the first input and output presentation modes. 8.The method of claim 3, wherein adjusting the first input and outputpresentation modes comprises modifying one or more of the specifiedquestion assistance settings or specified answer granularity options.9-20. (canceled)